Evaluating Attacker Risk Behavior in an Internet of Things Ecosystem

In cybersecurity, attackers range from brash, unsophisticated script kiddies and cybercriminals to stealthy, patient advanced persistent threats. When modeling these attackers, we can observe that they demonstrate different risk-seeking risk-averse behaviors. This work explores how an attacker’s risk seeking or averse behavior affects their operations against detection-optimizing defenders in Internet of Things ecosystem. Using evaluation framework which uses real, parametrizable malware, develop a game is played by defender with suite malware parameterized be more aggressive stealthy. These results are evaluated under exponential utility according willingness accept risk. We find who must choose single strategy up front, gain actual than particularly cases where the better equipped two anticipate. Additionally, empirically confirm high-risk, high-reward scenarios beneficial like cybercriminals, while low-risk, low-reward